MTD-Map: Single-Stage Long-Term LiDAR Map Maintenance Framework via Mixture Transition Distribution

2026-06-28

AI Summary

This work addresses the challenge of maintaining consistent long-term LiDAR maps by unifying dynamic object removal and change detection—tasks typically treated independently in existing approaches. To this end, we propose MTD-Map, a single-stage map maintenance framework that jointly handles both tasks through explicit modeling of a mixed transition distribution (MTD), eliminating the need for task-specific modules. The method recursively encodes historical occupancy states to capture high-order temporal dependencies and incorporates a stability-driven adaptive update mechanism that effectively suppresses noise while preserving quasi-static structures. Experimental results demonstrate that MTD-Map achieves state-of-the-art performance in both dynamic object removal and change detection, while significantly reducing computational overhead, thereby validating its robustness and efficiency.

Abstract

While robust map maintenance has advanced significantly, existing studies have focused on specific tasks, especially dynamic object removal or change detection. In this paper, we take a holistic view of the map maintenance problem and propose MTD-Map, a single-stage framework that handles both dynamic object removal and change detection without separate task-specific modules. MTD-Map employs an explicit representation that compactly encodes the direction and duration of occupancy transitions through Mixture Transition Distribution (MTD) modeling. We develop a recursive MTD formulation that encodes historical occupancy patterns into an augmented state to capture high-order temporal dependencies. Furthermore, a stability-driven adaptive strategy balances noise suppression with the preservation of quasi-static structures. Extensive experiments verify that MTD-Map robustly removes dynamic objects and achieves competitive change detection performance, subsequently reducing computational costs. Our project page is available at: https://taeyoung96.github.io/mtd_map/.

Problem

• map maintenance
• dynamic object removal
• change detection
• LiDAR
• temporal dependencies

Innovation

• Mixture Transition Distribution
• single-stage framework
• LiDAR map maintenance
• temporal dependency modeling
• dynamic object removal